Heretofore, there has been performed gradation correction processing such as changing the brightness or contrast of an image which requires the improvement in image quality, using an image processing apparatus. In such an image processing apparatus, an operator recognizes a problem in the image subjectively, and determines parameters for improving the problem based on his/her experience, to thereby perform the gradation correction processing by trial and error.
However, since the gradation correction processing is performed by trial and error, sometimes the operator must perform the gradation correction processing repetitively, while changing the parameters diversely, until the intended image quality can be obtained. In this case, labor of the operator required for improving the image quality is considerable, and this problem is particularly noticeable in general users who use photo-retouching software or the like, since they do not have enough knowledge relating to image processing.
Recently, a technique for automatically performing image correction processing has been developed, in order to reduce the labor of the operator. With this technique however, since the parameters for the image correction processing are determined, without recognizing a main object in the image, a corrected image may be significantly different from the image quality that the operator intends.
In view of the problems described above, it is an object of the present invention to provide an image processing technique that can improve the image quality, while considerably reducing the labor of the operator, by determining automatically and highly accurately parameters for the image correction processing, corresponding to a feature quantity of the image.